In DBSCAN, a point is considered a core point if:

• A) It belongs to two clusters simultaneously.
• B) It is the first point selected.
• C) It has at least the minimum number of neighboring points within a specified radius.
• D) It is far from all other points.

Which two parameters are required by DBSCAN?

• A) Support and Confidence
• B) Eps and MinPts
• C) K and Mean
• D) Learning Rate and Epochs

What does DBSCAN stand for?

• A) Dynamic-Based Statistical Clustering and Analysis Network
• B) Density-Based Systematic Classification Algorithm
• C) Density-Based Spatial Clustering of Applications with Noise
• D) Data-Based Scanning Algorithm

A store discovers the rule: Laptop → Mouse (Confidence = 80%) If 100 customers purchased laptops, how many are expected to purchase a mouse as well?

• A) 20
• B) 80
• C) 50
• D) 100

What is the primary objective of the K-Means algorithm?

• A) Predict future values
• B) Find relationships between variables
• C) Group data into K clusters by minimizing within-cluster variance
• D) Classify data into predefined categories

Which measure indicates how frequently an itemset appears in the transaction database?

• A) Support
• B) Accuracy
• C) Confidence
• D) Lift

Optimization in machine learning refers to:

• A) Increasing storage capacity
• B) Finding parameters that minimize a cost function
• C) Drawing graphs
• D) Removing all data points

A supermarket manager wants to identify products that customers frequently buy together to improve store layout. Which data mining technique should be used?

• A) Clustering
• B) Association Rule Mining
• C) Regression
• D) Classification

A point lies within the Eps distance of a core point but does not have enough neighbors to be a core point itself. What is this point called?

• A) Noise Point
• B) Isolated Point
• C) Cluster Point
• D) Border Point

Suppose a company selects K = 3 for customer segmentation. What does the value 3 represent?

• A) Three clusters to be formed
• B) Three centroids per cluster
• C) Three iterations of the algorithm
• D) Three variables in the dataset

Which formula is commonly used to measure the closeness between a data point and a centroid?

• A) Regression Equation
• B) Mean Formula
• C) Bayes Formula
• D) Euclidean Distance Formula

What happens if the learning rate is too large?

• A) The algorithm may overshoot the minimum
• B) Nothing changes
• C) The algorithm stops immediately
• D) The model becomes perfect instantly

What is the first step in the K-Means algorithm?

• A) Calculate distances
• B) Initialize centroids
• C) Assign data points to clusters
• D) Evaluate cluster quality

A machine learning model is not improving because it keeps getting stuck at a point that is not the lowest possible error. What optimization challenge does this represent?

• A) Overfitting prevention
• B) Global convergence
• C) Data normalization
• D) Local minimum problem

The intuition behind Gradient Descent is similar to:

• A) Building a mountain
• B) Drawing a straight line
• C) Finding the fastest route to the bottom of a hill
• D) Solving a puzzle randomly

A retail company wants to divide its customers into groups based on annual spending and visit frequency. Which data mining technique would be most appropriate?

• A) K-Means Clustering
• B) Association Rules
• C) Regression
• D) Classification

A data analyst wants to identify clusters of customers in a dataset while also detecting outliers. Which clustering algorithm is most suitable?

• A) Naïve Bayes
• B) DBSCAN
• C) Decision Tree
• D) K-Means

A rule has high confidence but very low support. What does this imply?

• A) The rule has no relationship
• B) The rule is reliable but applies to very few transactions.
• C) The rule occurs frequently in the database.
• D) The rule is useless.

A rule generated from transaction data is: Bread → Butter What does this rule indicate?

• A) Butter causes customers to buy bread.
• B) Customers who buy bread are likely to buy butter.
• C) Bread and butter have the same price.
• D) Customers buy bread only when they buy butter.

In the update rule of Gradient Descent, what does α (alpha) represent?

• A) Total error
• B) Output variable
• C) Learning rate
• D) Gradient direction